Comments on: A random forest guided tour

被引：3
|
作者：
Hooker, Giles [1 ]
Mentch, Lucas [2 ]
机构：
[1] Cornell Univ, Dept Biol Stat & Computat Biol, Ithaca, NY USA
[2] Univ Pittsburgh, Dept Stat, Pittsburgh, PA USA
来源：
TEST | 2016年 / 25卷 / 02期
基金：
美国国家科学基金会;
关键词：
Random forests; Machine learning; Extrapolation; Variable importance; CLASSIFICATION;
D O I：
10.1007/s11749-016-0485-3
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
We discuss future challenges in developing statistical theory for Random Forests. In particular, we suggest that an analysis of bias and extrapolation is vital to understanding the statistical properties of variable importance measures. We further point to the incorporation of random forests within larger statistical models as an important tool for high-dimensional statistical inference.
引用
收藏
页码：254 / 260
页数：7
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